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American Strangle Options 美国扼杀期权
Applied Mathematical Finance Pub Date : 2020-05-03 DOI: 10.1080/1350486X.2020.1825968
Shi Qiu
{"title":"American Strangle Options","authors":"Shi Qiu","doi":"10.1080/1350486X.2020.1825968","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1825968","url":null,"abstract":"ABSTRACT In this paper, we show that the double optimal stopping boundaries for American strangle options with finite horizon can be characterized as the unique pair of solution to a system of two nonlinear integral equations arising from the early exercise premium (EEP) representation. The proof of EEP representation is based on the change-of-variable formula with local time on curves. After comparing the return of the alternative portfolio including an American call and an American put option, we find that it is more preferable for an investor to select American strangle options to hedge an underlying asset with high volatility.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"62 1","pages":"228 - 263"},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83257394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Electricity Price Forecasting with Neural Networks on EPEX Order Books 基于EPEX订单的神经网络电价预测
Applied Mathematical Finance Pub Date : 2020-05-03 DOI: 10.1080/1350486x.2020.1805337
Simon Schnürch, A. Wagner
{"title":"Electricity Price Forecasting with Neural Networks on EPEX Order Books","authors":"Simon Schnürch, A. Wagner","doi":"10.1080/1350486x.2020.1805337","DOIUrl":"https://doi.org/10.1080/1350486x.2020.1805337","url":null,"abstract":"ABSTRACT This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected total demand. Appropriate feature extraction for the order book data is developed proceeding from existing literature. Using cross-validation to optimize hyperparameters, neural networks and random forests are fit to the data. Their in-sample and out-of-sample performance is compared to statistical reference models. The machine learning models outperform traditional approaches.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"40 1","pages":"189 - 206"},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73262295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Structural Clustering of Volatility Regimes for Dynamic Trading Strategies 动态交易策略中波动机制的结构聚类
Applied Mathematical Finance Pub Date : 2020-04-21 DOI: 10.1080/1350486X.2021.2007146
A. Prakash, Nick James, Max Menzies, Gilad Francis
{"title":"Structural Clustering of Volatility Regimes for Dynamic Trading Strategies","authors":"A. Prakash, Nick James, Max Menzies, Gilad Francis","doi":"10.1080/1350486X.2021.2007146","DOIUrl":"https://doi.org/10.1080/1350486X.2021.2007146","url":null,"abstract":"ABSTRACT We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We use change point detection to partition a time series into locally stationary segments and then compute a distance matrix between segment distributions. The segments are clustered into a learned number of discrete volatility regimes via an optimization routine. Using this framework, we determine the volatility clustering structure for financial indices, large-cap equities, exchange-traded funds and currency pairs. Our method overcomes the rigid assumptions necessary to implement many parametric regime-switching models while effectively distilling a time series into several characteristic behaviours. Our results provide a significant simplification of these time series and a strong descriptive analysis of prior behaviours of volatility. Finally, we create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series and its past regimes, thereby making online risk-avoidance decisions at present.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"10 1","pages":"236 - 274"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83600265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection 机器学习中的序贯假设检验与原油价格跳跃大小检测
Applied Mathematical Finance Pub Date : 2020-04-19 DOI: 10.1080/1350486X.2020.1859943
Michael Roberts, I. Sengupta
{"title":"Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection","authors":"Michael Roberts, I. Sengupta","doi":"10.1080/1350486X.2020.1859943","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1859943","url":null,"abstract":"ABSTRACT In this paper, we present a sequential hypothesis test for the detection of the distribution of jump size in Lévy processes. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analysed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the data set, and the deterministic component is implemented to improve the Barndorff-Nielsen & Shephard model, a commonly used stochastic model for derivative and commodity market analysis.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"55 1","pages":"374 - 395"},"PeriodicalIF":0.0,"publicationDate":"2020-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91153770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Is the Variance Swap Rate Affine in the Spot Variance? Evidence from S&P500 Data 在即期方差中，方差互换率是仿射的吗?标准普尔500指数数据的证据
Applied Mathematical Finance Pub Date : 2020-04-05 DOI: 10.2139/ssrn.3571429
M. Mancino, Simone Scotti, Giacomo Toscano
{"title":"Is the Variance Swap Rate Affine in the Spot Variance? Evidence from S&P500 Data","authors":"M. Mancino, Simone Scotti, Giacomo Toscano","doi":"10.2139/ssrn.3571429","DOIUrl":"https://doi.org/10.2139/ssrn.3571429","url":null,"abstract":"ABSTRACT We empirically investigate the functional link between the variance swap rate and the spot variance. Using S&P500 data over the period 2006–2018, we find overwhelming empirical evidence supporting the affine link implied by exponential affine stochastic volatility models. Tests on yearly subsamples suggest that exponential mean-reverting variance models provide a good fit during periods of extreme volatility, while polynomial modelsare suited for years characterized by more frequent price jumps.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"154 1","pages":"288 - 316"},"PeriodicalIF":0.0,"publicationDate":"2020-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77332567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Spoofing and Price Manipulation in Order-Driven Markets 订单驱动市场中的欺骗和价格操纵
Applied Mathematical Finance Pub Date : 2020-03-03 DOI: 10.1080/1350486X.2020.1726783
Á. Cartea, S. Jaimungal, Yixuan Wang
{"title":"Spoofing and Price Manipulation in Order-Driven Markets","authors":"Á. Cartea, S. Jaimungal, Yixuan Wang","doi":"10.1080/1350486X.2020.1726783","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1726783","url":null,"abstract":"ABSTRACT We model the trading strategy of an investor who spoofs the limit order book (LOB) to increase the revenue obtained from selling a position in a security. The strategy employs, in addition to sell limit orders (LOs) and sell market orders (MOs), a large number of spoof buy LOs to manipulate the volume imbalance of the LOB. Spoofing is illegal, so the strategy trades off the gains that originate from spoofing against the expected financial losses due to a fine imposed by the financial authorities. As the fine increases, the investor relies less on spoofing, and if the fine is large, the investor does not spoof the LOB. The arrival rate of buy MOs increases because other traders interpret the spoofed buy-heavy LOB as an upward pressure on prices. When the fine is low, spoofing considerably increases the revenues from liquidating a position. Spoofing increases the PnL because (i) the investor employs fewer MOs to draw the inventory to zero and benefits from roundtrip trades, which stem from spoof buy LOs that are ‘inadvertently’ filled and subsequently unwound with sell LOs; and (ii) the midprice trends upward when the book is buy-heavy; therefore the spoofer sells the asset at better prices.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"42 1","pages":"67 - 98"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81641005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Numerical Ross Recovery for Diffusion Processes Using a PDE Approach 采用PDE方法的扩散过程数值Ross恢复
Applied Mathematical Finance Pub Date : 2020-03-03 DOI: 10.1080/1350486X.2020.1730202
L. von Sydow, J. Waldén
{"title":"Numerical Ross Recovery for Diffusion Processes Using a PDE Approach","authors":"L. von Sydow, J. Waldén","doi":"10.1080/1350486X.2020.1730202","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1730202","url":null,"abstract":"ABSTRACT We develop and analyse a numerical method for solving the Ross recovery problem for a diffusion problem with unbounded support, with a transition independent pricing kernel. Asset prices are assumed to only be available on a bounded subinterval . Theoretical error bounds on the recovered pricing kernel are derived, relating the convergence rate as a function of to the rate of mean reversion of the diffusion process. Our suggested numerical method for finding the pricing kernel employs finite differences, and we apply Sturm–Liouville theory to make use of inverse iteration on the resulting discretized eigenvalue problem. We numerically verify the derived error bounds on a test bench of three model problems.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"21 1","pages":"46 - 66"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91222861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Additive Processes with Bilateral Gamma Marginals 双侧伽马边际的加性过程
Applied Mathematical Finance Pub Date : 2020-01-30 DOI: 10.2139/ssrn.3528510
D. Madan, King Wang
{"title":"Additive Processes with Bilateral Gamma Marginals","authors":"D. Madan, King Wang","doi":"10.2139/ssrn.3528510","DOIUrl":"https://doi.org/10.2139/ssrn.3528510","url":null,"abstract":"ABSTRACT The Sato process associated with self decomposable laws at unit time is further generalized to an additive process with arbitrary innovation term structures. A second generalization to additive processes consistent with bilateral gamma marginal distributions is also made. The Sato process is a parametric special case of the two generalizations. This feature is exploited in defining calibration starting values. Calibration results are presented for days of daily data on SPY options. The deterministic innovation variance model makes a median improvement of in root-mean-square error over the Sato process. The comparable value for the general additive process is The Sato process relative to the general additive process overprices negative moves and underprices positive ones. The underpricing of negative moves decreases with maturity. On the positive side, the overpricing decreases with maturity. For negative moves, the overpricing is larger for smaller moves, while for positive moves the underpricing is larger for the larger moves.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"148 1","pages":"171 - 188"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79378197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Portfolio Optimization for Credit-Risky Assets under Marshall–Olkin Dependence 马歇尔-奥尔金依赖下的信用风险资产组合优化
Applied Mathematical Finance Pub Date : 2019-11-02 DOI: 10.1080/1350486X.2020.1727755
Jan-Frederik Mai
{"title":"Portfolio Optimization for Credit-Risky Assets under Marshall–Olkin Dependence","authors":"Jan-Frederik Mai","doi":"10.1080/1350486X.2020.1727755","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1727755","url":null,"abstract":"ABSTRACT We consider power/logarithmic utility maximization in a multivariate Black–Scholes model that is enhanced by credit risk via the Marshall–Olkin exponential distribution. On the practical side, the model results in an enhancement of the mean variance paradigm, which is easy to interpret and implement. On the theoretical side, the model constitutes a well-justified and intuitive mathematical wrapping to study the effect of extreme and higher-order dependence on optimal portfolios.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"9 1","pages":"598 - 618"},"PeriodicalIF":0.0,"publicationDate":"2019-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77988390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Numerical Method for Model-free Pricing of Exotic Derivatives in Discrete Time Using Rough Path Signatures 基于粗糙路径特征的离散时间奇异导数无模型定价的数值方法
Applied Mathematical Finance Pub Date : 2019-11-02 DOI: 10.1080/1350486X.2020.1726784
Terry Lyons, Sina Nejad, Imanol Perez Arribas
{"title":"Numerical Method for Model-free Pricing of Exotic Derivatives in Discrete Time Using Rough Path Signatures","authors":"Terry Lyons, Sina Nejad, Imanol Perez Arribas","doi":"10.1080/1350486X.2020.1726784","DOIUrl":"https://doi.org/10.1080/1350486X.2020.1726784","url":null,"abstract":"ABSTRACT We estimate prices of exotic options in a discrete-time model-free setting when the trader has access to market prices of a rich enough class of exotic and vanilla options. This is achieved by estimating an unobservable quantity called ‘implied expected signature’ from such market prices, which are used to price other exotic derivatives. The implied expected signature is an object that characterizes the market dynamics.","PeriodicalId":35818,"journal":{"name":"Applied Mathematical Finance","volume":"43 1","pages":"583 - 597"},"PeriodicalIF":0.0,"publicationDate":"2019-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81661166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
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